Dae-Kun Ahn

Papers

Total Citations

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About

Dae-Kun Ahn is a robotics researcher whose work focuses on advancing control systems for robotic manipulation and locomotion. His key research areas include visual servoing, sensor-based control, and legged robot navigation. Ahn’s major contributions are demonstrated through his work on visual feedback control for SCARA robot arms, where he derived rank conditions relating the image Jacobian to control performance, proving that increasing the number of visual features significantly improves accuracy in visual servoing. This foundational study has garnered 6 citations, highlighting its relevance to precision robotics. Additionally, Ahn explored stable control for legged robots using ultrasonic sensors, developing a binaural sensory pod with an ultrasonic emitter and receivers for obstacle avoidance. His implementation of programmed obstacle avoidance behavior on a micro-controller enabled successful navigation in cluttered environments, earning 4 citations. Through these studies, Ahn has contributed to improving robotic accuracy and autonomy, offering practical insights for students and researchers working on sensor integration and control strategies in robotics.